Computer-Implemented System And Method For Facilitating Accurate Glycemic Control By Modeling Blood Glucose Using Cloud-Based Circadian Profiles

ABSTRACT

A computer-implemented system and method for facilitating accurate glycemic control by modeling blood glucose using cloud-based circadian profiles is provided. Anti-hyperglycemic medications are categorized based on similar glucose lowering effects. A circadian profile for a diabetic patient is built by storing online at least two recent typical measurements of pre-meal and post-meal blood glucose, a dose of an anti-hyperglycemic medication, and the class of the anti-hyperglycemic medication. A model of glucose management through the circadian profile is created by estimating expected blood glucose values and their predicted errors at each of the meal periods, visualizing the expected blood glucose values and their predicted errors over time for each meal period in a log-normal distribution, and selecting one of the meal periods and, for each anti-hyperglycemic medication in the identified class, and modeling a change in the dose of the anti-hyperglycemic medication based on glucose lowering effects of the modeled change.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 13/559,547, filed Jul. 26, 2012, pending, the disclosure ofwhich is incorporated by reference.

FIELD

The present invention relates in general to glycemic management indiabetic patients and, in particular, to a computer-implemented systemand method for improving glucose management through cloud-based modelingof circadian profiles.

BACKGROUND

As a chronic and incurable disease, diabetes mellitus requirescontinuing care that lasts throughout the life of the patient. Bothcaregivers and patients alike are primarily expected to play an activerole in managing diabetes, regardless of form, whether Type 1, Type 2,gestational, or other. Secondarily, family, friends and peers in thediabetic disease state play an important behind-the-scenes supportingrole in ongoing glucose management. Diabetes patients are typicallycoached by their caregivers on lifestyle modification and educated tounderstand the affects of diet, especially carbohydrates, body weight,physical activity, medications, and stress on their diabetic condition.Diabetes patients are also trained and encouraged to regularly test andrecord their blood glucose levels. In addition, medication-treatedpatients learn to undertake daily self-administration of medicationsand, where appropriate, determine corrective medication dosing tocounteract postprandial glycemic rise. All diabetes patients areexpected to document their self-care in a daily diary that typicallychronicles self-monitored blood glucose values, medications, physicalactivity, and dietary intake.

In turn, caregivers follow their diabetes patients on a periodic basisand work to ensure their compliance with the consensus guidelines andmandatory targets (CG&MT), which have been formulated and are regularlyupdated by the American Association of Clinical Endocrinologists (AACE)and the American College of Endocrinology (ACE), as well as the AmericanDiabetes Association (ADA). At each patient consultation, a caregivermay evaluate the patient's daily diary to identify patterns in thepre-meal data, which can include examining particular examples of thepatient's actions to determine underlying causes for any outcomessuffered, above all, episodes of hypoglycemia. Additionally, thecaregiver will normally test the patient's level of glycated hemoglobin(“A1c”) to establish accord with the current CG&MT target forwell-managed diabetes. As needed, the caregiver may adjust the patient'soral anti-diabetic medications or insulin dosing to hopefully move thepatient's blood glucose and A1c levels closer to the mandated targets.

The roles respectively performed by caregivers and their diabeticpatients form a “circle of care” that requires each patient to providetheir own data and do those actions necessary that together allow thecaregivers to effectively manage the patient's diabetic condition. At aminimum, each patient is expected to self monitor their blood glucoselevels and comply with each caregiver's instructions. Obversely, thecaregivers are expected to monitor the patient's condition and provideapt guidance through changes in medications and lifestyle as needed toachieve perfect diabetes control as mandated in the various guidelines.

Notwithstanding, the circle of care generally remains incomplete.Conventional diabetes management efforts are in practice remarkablyretrospective due to the significant focus on past patient condition, asseen through the patient's self-monitored blood glucose values thatordinarily extend back over several prior months. In turn, armed at bestwith the historical values of blood glucose testing, as sometimesconfirmed by A1c results, a caregiver endeavors to control the futuredirection of ongoing diabetes treatment typically for the next severalmonths until the next consultation. This control is exercised chiefly bymaking adjustments to medications, typically focused on insulin, withthe intent of somehow moving patient blood glucose levels and A1c totarget, and often without demanding data more reflective of thepatient's true condition at the time of consultation.

In practice, the circle of care also suffers from imbalance. From acaregiver perspective, the circle of care is generally viewed as aclosed two-way exchange between the caregiver and the patient, eventhough the caregiver often refers the patient to outside diabeteseducational and support resources. Conversely, from a patientperspective, the circle of care simply begins with the caregiver andcontinues with the support community that helps the patient cope withand manage the disease. Although the contribution of a support communityto a patient's well being is merely intrinsic and is not stronglyfactored into the caregiver's immediate treatment approach, such supportnevertheless is invaluable and provides the patient with a venue forsharing experiences and imparts understanding, motivation and hope inspite of the chronic nature of diabetes.

The incompleteness of the circle of care also contributes to the dilemmafaced by caregivers in managing diabetes, which suggests thatsatisfactory glycemic control is seemingly only achievable withunsatisfactory risk of hypoglycemia, as well as the converse. The CG&MTrecommends a fasting blood glucose level of less than 110 mg/dL(non-fasting less than 140 mg/dL) and A1c between 6% and 7%, withpatients generally being asked to strive for A1c of less than 7% (andless than 6.5% according to other standards). Achieving these goals,however, carries the adverse consequence of increasing the risks oftreatment-related hypoglycemia, which caregivers counter by changingdiet or medication dosing that then shifts that patient's blood glucoselevel outside the CG&MT target range. Consequently, a self-reinforcingvicious cycle is formed, as increased medication dosing to reduceglycemic values into mandated target ranges results in increasedhypoglycemic risk that a patient must counteract by eating more with anensuing gain in body weight that induces further diabetes medicationdosing change.

Therefore, a need remains for providing an improved approach to glycemiccontrol that shifts the focus of diabetes management efforts away fromretrospective blood glucose histories to recent and representativeglycemic indications that better tie caregiver efforts and glucosemanagement to actual, realized and timely patient need, and which opensup the circle of care to the support community that helps patients indealing with diabetes day-to-day.

SUMMARY

A predictive circadian profile that accurately models expected bloodglucose values and their expected error can be created by using only theSMBG data stored in a near-term observational time frame, typically aweek, immediately preceding the next caregiver consultation. Onlyvalidated (recent and typical) SMBG values are used in predictingexpected glycemic outlook, thereby ensuring a reliable model. Thecircadian profile can be shared online with the patient's supportcommunity, with the assistance of social networking media and theso-called “cloud” computing infrastructure, and their feedback can befactored into ongoing glucose management efforts. During patientconsultations, the caregiver can explore changes to medication dosing,which can include all manner of anti-diabetes drugs, including insulinand oral agents, with confidence that the new dosing will both move thepatient's glycemic control into the desired target ranges and avoid thedeleterious risk of treatment-related hypoglycemia.

One embodiment provides a computer-implemented method for facilitatingaccurate glycemic control by modeling blood glucose using cloud-basedcircadian profiles. A plurality of meal periods that each occur each dayare defined. A plurality of classes that each class comprises a set ofanti-hyperglycemic medications that has similar glucose lowering effectsare defined. A circadian profile for a diabetic patient is built bychoosing an observational time frame for the circadian profilecomprising a plurality of days that have occurred recently, storingonline at least two sets of pre- and post-meal period data that compriseblood glucose levels, doses of anti-hyperglycemic medications that wererespectively taken during each of the meal periods for which the bloodglucose levels were recorded in a cloud computing infrastructure, andthe classes to which the anti-hyperglycemic medications are comprised. Amodel of glucose management through the circadian profile for thediabetic patient for each of the anti-hyperglycemic medications in theclass is created by validating access to the circadian profile, defininga modeling period comprising a plurality of days, which each comprisesthe same plurality of the meal periods that occurred each day in theobservational time frame, estimating expected blood glucose values andtheir predicted errors at each of the meal periods from the bloodglucose levels in each date in the validated circadian profile thatrespectively occur at the same set times, visualizing the expected bloodglucose values and their predicted errors over time for each meal periodin a log-normal distribution, selecting one of the meal periods and, foreach anti-hyperglycemic medication in the class, modeling a change inthe dose of the anti-hyperglycemic medication for the selected mealperiod. Further, the modeling the change in the dose of theanti-hyperglycemic medication includes obtaining glucose lowering effectof the modeled change in the dose of the anti-hyperglycemic medicationand glucose lowering effects of other anti-hyperglycemic medications inthe class, normalizing the glucose lowering effect of the modeled changein the dose of the anti-hyperglycemic medication with the glucoselowering effects of the other anti-hyperglycemic medications in theclass, propagating the normalized glucose lowering effect over time forthe modeled change in the dose of the anti-hyperglycemic medication tothe expected blood glucose values, beginning with the selected mealperiod and continuing with each of the meal periods occurringsubsequently in the modeling period, the normalized blood glucoselowering effect being adjusted in proportion to the set time of eachsubsequent meal period until the normalized blood glucose loweringeffect is exhausted, and visualizing the expected blood glucose valuesas propagated and their predicted errors in the log-normal distribution.

A further embodiment provides a computer-implemented system forfacilitating accurate glycemic control by modeling blood glucose usingcloud-based circadian profiles. The system includes anelectronically-stored database maintained in a cloud computinginfrastructure and comprising a plurality of records, each recordincluding a circadian profile and including a plurality of meal periodsthat each occur each day and divide each circadian profile into the mealperiods, a plurality of classes that each class comprises a set ofanti-hyperglycemic medications that has similar glucose loweringeffects, an observational time frame comprising a plurality of days thathave occurred recently, at least two of typical measurements of pre-mealand post-meal self-measured blood glucose that were recorded at each ofthe meal periods that occurred each day in the observational time frame,a dose of an anti-hyperglycemic medication that was taken during each ofthe meal periods for which the blood glucose measurements were recorded,and one of the classes to which the dose of the anti-hyperglycemicmedication is comprised. The system further includes a user interfacefor creating a model of glucose management through the circadian profilefor the diabetic patient for each of the anti-hyperglycemic medicationsin the class, including an executable application configured to modelthe glucose management, further including a validation module configuredto validate access to the circadian profiles through the cloud computingenvironment, a model period module configured to define a modelingperiod comprising a plurality of days, which each comprises the sameplurality of the meal periods that occurred each day in theobservational time frame, a collection module configured to collect theblood glucose measurements, upon validation, comprising each of the mealperiods, a statistical engine configured to determine expected bloodglucose values and their predicted errors at each of the meal periodsfrom the blood glucose measurements based on the meal periods in thecircadian profile, a log-normal distribution module configured tovisualize the expected blood glucose values and their predicted errorsover time for each meal period in a log-normal distribution, a changemodeling module configured to select one of the meal periods and, foreach anti-hyperglycemic medication in the class, to model a change inthe dose of the anti-hyperglycemic medication for the selected mealperiod, further including an effect module configured to obtain glucoselowering effect of the modeled change in the dose of theanti-hyperglycemic medication and glucose lowering effects of otheranti-hyperglycemic medications in the class, a normalization moduleconfigured to normalize the glucose lowering effect of the modeledchange in the dose of the anti-hyperglycemic medication with the glucoselowering effects of the other anti-hyperglycemic medications in theclass, a propagation module configured to propagate the normalizedglucose lowering effect over time for the modeled change in the dose ofthe anti-hyperglycemic medication to the expected blood glucose values,beginning with the selected meal period and continuing with each of themeal periods occurring subsequently in the modeling period, thenormalized blood glucose lowering effect being adjusted in proportion tothe set time of each subsequent meal period until the normalized bloodglucose lowering effect is exhausted, a visualization module configuredto visualize the expected blood glucose values as propagated and theirpredicted errors in the log-normal distribution, and a control panel foroperating the change in the dose of the anti-hyperglycemic medication.In addition, the system includes a display for displaying the model ofglucose management through the circadian profile.

For certain types of diabetes patients, the approach removes the needfor repeated SMBG testing throughout each day and extending over theentire course of time separating caregiver consultations. Type 2diabetes patients, for instance, would only need to collect a minimum oftwo SMBG results per meal period in the week prior to consultation.Moreover, with this approach, glycemic management can be performed in anintermittent “batch” processing fashion and not in real time.

The approach also enhances caregiver confidence, as the predicted bloodglucose levels and their error ranges are based on recent and typicalpatient data. The caregiver is then able to treat to target and safelyprescribe medication dosing changes, which can include all manner ofanti-diabetes drugs, including insulin and oral agents, with a highdegree of confidence of attaining the results desired.

Finally, the approach broadens the span of the circle of care byincluding the patient's support community as an integral part of glucosemanagement efforts. Peers in the disease state can share in theirexperiences online and provide inputs that can compliment and reinforceformal caregiver guidance. Peers can also share and compare theircircadian profiles; earn rewards, such as badges, stars or status in theonline community for reaching milestones; and compete with peers forperfect diabetes control.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a computer-implemented system forimproving glucose management through cloud-based modeling of circadianprofiles, in accordance with one embodiment.

FIG. 2 is a flow diagram showing a computer-implemented method forimproving glucose management through cloud-based modeling of circadianprofiles, in accordance with one embodiment.

FIG. 3 is a flow diagram showing a routine for assembling meal perioddata for use in the method of FIG. 2.

FIG. 4 is a user interface diagram showing, by way of example, aninteractive screen for a circadian profile for use in the system of FIG.1.

FIG. 5 is a flow diagram showing a routine for storing and evaluatinguploaded meal period data for use in the method of FIG. 2.

FIG. 6 is a flow diagram showing a routine for visualizing expectedblood glucose values and predicted errors for use in the routine of FIG.5.

FIG. 7 is a flow diagram showing a routine for superimposing targetranges over the expected blood glucose values and predicted errors foruse in the routine of FIG. 5.

FIG. 8 is a user interface diagram showing, by way of example, aninteractive screen for visualizing and evaluating the expected bloodglucose values and predicted errors for use in the system of FIG. 1.

FIG. 9 is a flow diagram showing a routine for propagating anincremental change in medication dosing for use in the routine of FIG.5.

FIGS. 10 and 11 are user interface diagrams showing, by way of example,interactive screens for modeling incremental changes in medicationdosing for use in the system of FIG. 1 respectively before and aftersuperimposing the target ranges.

FIG. 12 is a user interface diagram showing, by way of example, aninteractive screen for a circadian profile for use in a furtherembodiment of the system of FIG. 1.

DETAILED DESCRIPTION

Ideal glycemic control in a diabetes patient occurs when the averagevalue of self-measured blood glucose (SMBG) at each point in a circadiandiabetes profile falls within a specific target range. The efficacy ofcurrent diabetes control can be improved to help make possible idealglycemic management by harnessing the statistical properties of bloodglucose and biologic rhythmicity, when represented as categorical, nottime series, data, to predict circadian profiles of expected bloodglucose values. FIG. 1 is a block diagram showing a computer-implementedsystem 10 for improving glucose management through cloud-based modelingof circadian profiles, in accordance with one embodiment. Circadianprofiles close the heretofore-incomplete circle of care and remove thedanger of clinical diabetes medication prescription errors, which havebeen caused by overly retrospective glycemic focus and chiefly by makingadjustments to medications, typically focused on insulin.

The system 10 is loosely grouped into three sets of components. First, apatient-oriented structured database 14 stores SMBG values, medicationdosing for all types of anti-diabetes drugs, including insulin and oralagents, and related information for a diabetes patient 11 on a portablemedia device 12, such as a USB flash drive or other form ofnon-transitory removable computer-readable storage medium, such asdescribed in commonly-assigned U.S. Pat. No. 8,744,828, issued on Jun.3, 2014, the disclosure of which is incorporated by reference. The SMBGmeasurements are typically obtained from a conventional glucose meter(“glucometer”). Once a complete circadian profile has been created, asfurther described infra with respect to FIG. 2, the database 14 isuploaded from a personal or laptop computer 13 or mobile computingdevice (not shown), such as a smart phone, onto a wide area public datanetwork 21, such as the Internet, or other network infrastructure. Acaregiver-centric consultation program 15 executes on a personal orlaptop computer 13 or mobile computing device and enables a diabetespatient 11 to access the database 14 as stored on the portable mediadevice 12. To upload the database 14, the portable media device 12 isinterfaced via a built-in data interface port 16, such as a USBinterface plug or other wireless or wired adapter, with the personal orlaptop computer 13, mobile computing device, or other compatiblecomputing device, which then loads the necessary program, library anddata files from the portable media device 12.

In a further embodiment, the database 14 could be integrated into aglucometer 17 or other type of portable blood glucose testing devicewith onboard data collection capabilities, such as described incommonly-assigned U.S. Pat. No. 8,756,043, issued on Jun. 17, 2014, thedisclosure of which is incorporated by reference. The glucometer 17 is a“smart” suitably-programmed glucometer that includes an internal memorythat electronically records the results of each blood glucose test,along with the date and time of testing, into the database 14.Additionally, the glucometer 17 has a visual display 19 and a set ofinput controls 18 that together form a user interface through which SMBGtesting and diabetes medication dosing data, as well as other optionalbut useful patient information, can be entered. When in use by thepatient 11, the glucometer 17 calculates and displays blood glucoselevels by reading a disposable test strip 20 upon which the patient 11has placed a drop of blood. With the reading of the test strip 20, thepatient 11 uses the input controls 18 to validate and then identify thecurrent category of meal period and the SMBG measurement is then storedby the glucometer 17 into the patient's circadian profile under theindicated meal period category. The patient's diabetes management datais later offloaded to a suitably-programmed computer, such as thepersonal or laptop computer 13 or mobile computing device via a built-indata interface port, such as a USB interface plug or other wireless orwired adapter, which is then uploaded onto the network 21. In a furtherembodiment, the glucometer 17 is provided as an external “glucophone,”that is, a smart phone that has a built-in or add-on glucometer, and thedatabase 14 and the program 15 are stored on and maintained by theglucophone. Still other types of devices for storing diabetes managementdata into the database 14 are possible, for instance, a tablet ornetwork computer. In addition, other intermediary devices for uploadingthe diabetes management data onto the network 21 are possible, such as aWeb-enabled smart phone that has a built-in or add-on glucometer formeasuring SMBG levels and is able to upload data directly onto thenetwork 21.

Second, each patient's diabetes management data is stored and evaluatedonline. This online storage and evaluation of patient data utilizes theflexibility and ubiquity of a so-called “cloud” computing infrastructurethat dissociates the need for supplying dedicated computing resources toeach patient and caregiver and instead entrusts the provisioning of datastorage and computation to a distributed network-based service paradigm.The patient's data are externally stored in an online database 25, whichis secured, private and password-protected, and both current andpreviously stored data can be accessed. In one embodiment, the diabetesmanagement data is stored in an online database 25, which is maintainedin storage 24 that is interfaced over the network 21, although thedatabase 25 could also be stored using several different storagearrangements that could each be tied to a single physical data server orto different servers under a common data access front-end. Still otheronline structurings of the storage of the database 25 are possible.

Finally, a server 22 executes a diabetes management server 23 (DMS)through which online users 27, including the caregiver and patient 11,as well as the patient's support community, are able to collaborativelyaccess, evaluate and share the diabetes management data in the database25 online, as further described infra with reference to FIG. 5. Throughthe DMS 23, the users 27 can execute a caregiver-centric consultationprogram 28 on a personal or laptop computer 29, mobile computing device,or other network-capable device, including a Web-enabled smart phone.The DMS 23 validates each user 27 to ensure legitimate access andretrieves patient diabetes data from the database 25 in response to theprogram 28, which can then generate predictive circadian profiles foruse in following diabetes patients and ensuring their CG&MT compliance,but without the dilemma of treatment-induced increased hypoglycemicrisk. Upon initial execution, the database 25 and program 28 arepersonalized with the patient's and his caregiver's demographicinformation, as appropriate, after which the patient can add additionalSMBG values, lifestyle, and diabetes medication details for all types ofanti-diabetes drugs, including insulin and oral agents. His caregiverperforms a similar installation process and executes the program 28,which provides circadian profile-based predictions of blood glucosevalues and their expected errors, incremental suggestions and modelingof changes to medication dosing, and diabetes patient counseling points.

The database 14 and program 15 collaboratively facilitate theachievement of improving glycemic management by respectively chroniclingrelevant patient self-management efforts and predictively modelingglycemic outcomes for caregiver review and utilization. FIG. 2 is a flowdiagram showing a computer-implemented method 30 for improving glucosemanagement through cloud-based modeling of circadian profiles, inaccordance with one embodiment. The method 30 can be implemented insoftware, such as through the databases 14, 25 and programs 15, 28, andexecution of the software can be performed on a computer system 10, suchas described supra with reference to FIG. 1, as a series of process ormethod modules or steps.

By way of overview, a patient's SMBG measurements and accompanyingdosing of diabetes medications, including insulin and oral agents, areentered into a circadian profile that is stored in the structureddatabase 14. The circadian profile is implemented using a format thataffords a one-to-one correspondence with the CG&MT mandated targetranges of blood glucose values and is organized as data records in thedatabase 14. The circadian profile structures daily SMBG measurementsand medication dosing into a data series of pre-meal and timed post-mealcategories. A day is modeled as a complete data series, even though theactual patient data within a particular “day” may actually have beencollected on different calendar days falling within the observationaltime frame. In one embodiment, each modeled day is divided into mealperiods for breakfast, lunch and dinner, and one additional “meal”period from pre-bedtime through overnight to pre-breakfast, which isactually a period of fasting. Each data series includes one pre-mealSMBG value and diabetes medication dosing for each of breakfast, lunch,and dinner (three SMBG values) and one timed post-meal period SMBG valuealso for each of breakfast, and dinner (three more SMBG values), plusone timed post-meal period SMBG value both pre-bedtime and overnight. Inaddition, notations on daily lifestyle chronicling physical activity,diet and stress at each meal period, and daily body weight can beincluded in the data record. Still other patient- and treatment-relateddata can also be stored in the database 14.

The program 15 implements a statistical engine that regards the bloodglucose values as categories and not as a time series, that is, temporalevents based on actual “clock” time. A time series creates a time vectorproblem. For example, consider the averaging of continuous diurnalglucose readings for a patient's breakfast. On one day, say, Saturday,breakfast may occur at 6:30 am, while the next day, the patient decidesto sleep in and breakfast may consequently occur at 8:15 am. The lateroccurrence of Sunday's breakfast at 8:15 am causes that diurnal glucosereading to temporally coincide with the peak post-meal diurnal glucosereading of Saturday, which causes the averaging of the wrong bloodglucose values. To avoid the time vector problem, the blood glucose datais transformed from a time series axis to a category axis, where thecorrect pre- and timed post-meal blood glucose values are collectedaccording to their descriptive labels, not clock time. The use ofcategories enables the blood glucose value to be both predictable andmodelable using a log-normal statistical distribution for a model day.

A further advantage of using a category axis is being able toautomatically synchronize bolus and basal doses to both span the timeperiod beginning at just before a meal period all the way through tojust before the next meal period. Conventional insulin pumpmanufacturers often program basal rates according to clock times thatmay not necessarily correspond to pre-meal times. Diabetes patients 11who use an insulin pump are typically given the freedom to change theirmeal times or even skip meals altogether. Consequently, a potentiallyhazardous situation could arise when a patient 11 delays or skips a mealthat normally includes an increase or “jump” in basal rate. If thechange in basal rate was triggered by the patient 11 indicating the realstarting point of a meal, patient safety would be restored because thebasal rate would not change until the actual start of the meal.

The formation of a circadian profile begins with assembling meal perioddata, which includes both SMBG measurements and any diabetes medicationsdosed, including insulin and oral agents, for the patient 11 for mealperiods occurring over a recent observational time frame, typically fromthe last seven days, into the database 14 (step 31), as furtherdescribed infra with reference to FIG. 3. The meal period data is thenorganized into a circadian profile (step 32), as further described infrawith reference to FIG. 4. Meal period data is cumulatively collectedfrom the patient 11 (step 33). Additional data is accepted from thepatient 11 and preferably at least two typical measurements of pre-mealand post-meal SMBG are eventually collected for each meal period. Uponcompletion, the meal period data, including the completed circadianprofile, is uploaded from the personal or laptop computer 15 or mobilecomputing device to the network 21 (step 34) for storage and evaluationin a “cloud” computing infrastructure (step 35), as further describedinfra with reference to FIG. 5.

Meal periods form a set of categories within which SMBG values anddiabetes medication, including insulin and oral agents, are stored andstatistically analyzed. FIG. 3 is a flow diagram showing a routine 40for assembling meal period data for use in the method 30 of FIG. 2.First, the available SMBG values are collected (step 41). In a furtherembodiment, the SMBG values may be pre-populated and simply verifiedonce the user validates. For instance, wireless-capable glucometerscould transmit SMBG readings automatically to populate the patient'sdatabase as maintained in cloud storage. Each of the SMBG values issystematically validated (steps 42-48), as follows. To ensure accurateprediction of glycemic outcome, only recent and typical SMBG values areallowed. Recent (step 43) means that the SMBG value was obtained duringthe seven days preceding the next caregiver consultation. Other timeframes are possible, but increasing the window beyond seven daysundermines the value and meaningfulness of the SMBG data as reflectiveof current actual glycemic condition. Typical (step 44) means that eachof the SMBG values is without qualifications or exception. For instance,an SMBG measurement taken following a substantial Thanksgiving Day feastwould be atypical and would not be representative of the patient'stypical diet.

When entering data, the patient 11 has the ability to flag SMBG values(step 49) as not being either recent (step 43) or typical (step 44)either by performing a point-and-click operation with his mouse or otherpointing device, or by manually typing comments in an editable commentsfield in the circadian profile. The patient 11 also identifies theapplicable meal period category, for instance, pre-breakfast, and theSMBG value is retained (step 45). The ability to flag atypical SMBGvalues enables a patient 11 to associate a particular SMBG value withone or more events that can help explain the departure from expected andtypical SMBG levels, such as a high or low carbohydrate intake, exerciseor physical activity, or stress, as further described below withreference to FIG. 11. These explanatory events can be graded in levelsrelative to their normal baseline. In a further embodiment, flaggedatypical SMBG values can be differentially weighted for use in thedetermination of expected blood glucose values and predicted errors, asfurther described infra, discarded or used in any other way.

If the SMBG value is both recent and typical, the patient 11 identifiesthe applicable meal period category, for instance, pre-breakfast, andthe SMBG value is retained (step 45). Data entry can be done all atonce, or episodically, as convenient. As the program 15 can modelinsulin and most oral (tablet) or injected anti-diabetes drugs, thepatient 11 also identifies any diabetes medications, including oral orinjected anti-diabetic agents and insulin doses, which were taken oradministered about the time that the blood glucose was measured (step46). Both basal and bolus insulin dosing, plus optionally, the site ofinsulin injection on the patient's body, are identified. Insulininjection site provides a point of discussion between the caregiver andthe patient 11 during consultation in light of the affect that injectionsite can have on insulin absorption and therefore the rate of glycemicregulation. The SMBG value and the diabetes medication dosing are storedinto the database 14 under the meal period category that was identifiedby the patient 11 (step 47).

The ability to flag atypical SMBG values enables a patient 11 toassociate a particular SMBG value with one or more events that can helpexplain the departure from expected and typical SMBG levels, such as ahigh or low carbohydrate intake, exercise or physical activity, orstress, as further described below with reference to FIG. 12. Theseexplanatory events can be graded in levels relative to their normalbaseline. In a further embodiment, flagged atypical SMBG values can bedifferentially weighted for use in the determination of expected bloodglucose values and predicted errors, as further described infra,discarded or used in any other way.

In one embodiment, only a single type of basal insulin, that is,longer-acting insulin with a physiologic mechanism of action principallyspanning one half day to no more than one full day, and a single type ofbolus insulin, that is, shorter-acting insulin with a physiologicmechanism of action principally spanning no more than three to eighthours, are accepted into the circadian profile. Other types oflonger-acting and short-acting drugs in addition to or in lieu ofinsulin could also be accepted. However, dosing of different types ofinsulin having the same temporal mechanism of action, such as multiplesimultaneous or overlapping short-acting insulin, is not permitted, asthe net affect of arbitrarily combinable multiple insulin dosing isambiguous and cannot be modeled with sufficient predictive certainty.

In a further embodiment, glucose lowering drugs, including shorter-,intermediate-, and longer-acting classes of anti-diabetes drugs,particularly oral hypoglycemia drugs, are modeled in addition to or inlieu of insulin. These medications include insulin sensitizers,including biguanides and thiazolidinediones; secretagogues, such assulfonylureas and non-sulfonylurea secretagogues; alpha-glucosidaseinhibitors; and peptide analogs, for instance, injectable incretinmimetics, injectable Glucagon-like peptide analogs and agonists, gastricinhibitory peptide analogs, dipeptidyl Peptidase-4 inhibitors, andinjectable Amylin analogues. Other types of glucose lowering medicationscould also be accepted.

Finally, a minimum of two SMBG values per meal period are needed to forma complete circadian profile, one pre-meal measurement and one timedpost-meal measurement, which, for statistical purposes, should berepeated a minimum of two times apiece for a total of 16 SMBG values,although more data, up to the maximum possible over a recent time frame,are possible. In one embodiment, a maximum of 56 SMBG values can beaccepted, which account for one pre-meal SMBG value for breakfast,lunch, and dinner (three SMBG values) and one timed post-meal periodSMBG value also for breakfast, lunch, and dinner (three more SMBGvalues), also a bedtime and an overnight (two SMBG values), over anentire seven-day week. The patient 11 continues to enter SMBG data (step50) until all available data up to that time have been entered.

Conventional approaches to diabetes management are often retrospectivein that changes in treatment are primarily based on historical, ratherthan recent, glycemic outcomes. In contrast, a circadian profile, asdescribed herein, shifts the focus to recent indicators of glycemiccondition and only for typical meal periods, which enables accurateprediction of short-term blood glucose and A1c outcomes. FIG. 4 is auser interface diagram showing, by way of example, an interactive screen60 for a circadian profile 61 for use in the system 10 of FIG. 1. Theinteractive screen 60 is generated by the program 15 for use by both thepatient 11 and the caregiver during consultations.

The circadian profile fits within the “three-legged stool” metaphor ofclinical diabetes management that focuses on body weight, A1c level andglycemic management. The creation of each circadian profile begins withassembling and organizing SMBG values and diabetes medication, as wellas other relevant information that is stored into the database 14. Thepatient's and caregiver's demographics 62 are entered as an initialstep. The remainder of the circadian profile 61 contains patientinformation that is organized under a series of pre-meal and timedpost-meal categories 63. In one embodiment, eight categories 63 of mealperiods are defined for breakfast, lunch and dinner: pre- and timedpost-meal, pre-bedtime and overnight periods, although othercategory-based series are possible, including mid-meal periods. Withineach category 63, the patient's body weight, SMBG values 64 and theirtimes of measurement are entered, plus any diabetes medication 65 thatwas taken or administered. In addition, for those patients who are oninjections of insulin, the site of injection is also entered, whichprovides a talking point during patient consultation. Finally, thepatient 11 can enter optional comments 66 on lifestyle, includingcarbohydrate estimate (“CHO”), exercise or physical activity level(“EX”), stress, and so forth. The lifestyle comments are also points ofpossible discussion with the caregiver. Other patient data can also becollected, like blood pressure and resting heart rate. In addition toallowing each patient's database to be portable and ensuring that theirdata is virtually unloseable, the storage of each patient's meal perioddata in the “cloud” allows collaborative online modeling of predictivecircadian profiles through social networking, which can be used andshared by the caregiver and patient 11, as well as his supportcommunity. FIG. 5 is a flow diagram showing a routine 70 for storing andevaluating uploaded meal period data for use in the method 30 of FIG. 2.The wider availability of the diabetes management data beneficiallyenlarges the circle of care to include the support community that helpspatients in dealing with diabetes on a day-to-day basis. Patients gainaccess to peer support and encouragement in a virtual community, whichmimics a traditional support group setting. Patients can learn from eachother's experiences with glucose management, which allows them to avoidsimilar pitfalls in their own medication or lifestyle management.Patients can also share their circadian profiles, either to requestinput from the support community on how to improve their glycemiccontrol or as a point of pride to display the circadian profile aftersuccessfully achieving glycemic control falling within target range. Inaddition, patients can form groups within their social network based onfriends, coworkers, and individuals with a similar type of diabetes ormedication. These social network groups can participate in competitionsfor achieving outcomes, such as glucose control, A1C targets,elimination of hypoglycemia, and so forth. They can also earn rewards inthis virtual community for achieving milestones or outperforming peers.

To safeguard access to a particular patient's data, every user operateswithin a secure environment (steps 71-77) that begins with validatingeach user's credentials (step 72) through the DMS 23 (shown in FIG. 1).Only patient data for which the user has received permission to view canbe retrieved (step 73). Once retrieved, the patient data can be used togenerate glycemic predictions and visualizations. The short-term,typically 7-day, time frame over recent glycemic management provided bythe circadian profile has been shown to allow accurate prediction ofblood glucose outcomes. As a result, a model of the expected values ofnear-term blood glucose values and their predicted errors can be createdand visualized (step 74), as further described infra with reference toFIG. 6. The visualization identifies those meal periods that areaccompanied by a predicted risk of hypoglycemia or occurrence ofhyperglycemia, which the caregiver is urged to address with the patient11 during consultation. In addition, the CG&MT target ranges or, ifpreferred, the caregiver's targets for the patient 11, can besuperimposed over the visualized blood glucose prediction to enable thecaregiver to evaluate likely excursions from well-managed glycemic care(step 75), as further described infra with reference to FIG. 7.

Through the visualized glycemic outcome model, incremental suggestionson possible changes to medication dosing can be provided, which thecaregiver can interactively explore to evaluate likely near-term affecton the patient 11. The program 15 supports the interactive explorationand modeling of all manner of anti-diabetes drugs, including insulin,other injectable medications and oral agents. As selected by thecaregiver, potential changes in medication dosing are visuallypropagated over the blood glucose prediction (step 76), as furtherdescribed infra with reference to FIG. 9. Other steps to further thepatient consultation are possible, such as reviewing weight controlthrough body mass index calculation and body weight trend analysis.

The categorization of recent typical SMBG values into a circadianprofile enables accurate prediction and modeling of near-term bloodglucose and A1c levels. FIG. 6 is a flow diagram showing a routine 80for visualizing expected blood glucose values and predicted errors foruse in the routine 70 of FIG. 5. Each of the sets of meal period data isevaluated and modeled (steps 81-84), as follows. The expected bloodglucose value and predicted error for each meal period on the categoryaxis is first determined and a model of the expected blood glucosevalues and their respective predicted errors by meal periods is createdfor a model day (step 82). During the statistical determination of theexpected blood glucose values and predicted errors, all SMBG values maybe treated as having equal weight in terms of their respective influenceon the prediction and modeling of near-term blood glucose and A1clevels. In a further embodiment, individual SMBG values can be flaggedand differentially weighted based on a weighting criteria, such as usedto flag atypical SMBG, as discussed supra, which causes the model toreflect the relative influence of each SMBG value based on itsrespective weight. Other ways of emphasizing or deemphasizing factorsaffecting SMBG monitoring are possible.

A seven-day window is used to generate the model. Recall that areplicated minimum of two SMBG values per meal period is preferred,although more data within the seven-day observational time frame arebelieved to improve accuracy. The statistical methods for performing thenear-term blood glucose level prediction has been clinically validatedfor both efficacy and safety, such as described in A. M. Albisser etal., Home Blood Glucose Prediction: Validation, Safety, and EfficacyTesting in Clinical Diabetes, Diabetes Tech. Ther., Vol. 7, pp. 487-496(2006); and A. M Albisser et al., Home Blood Glucose Prediction:Clinical Feasibility and Validation in Islet Cell TransplantationCandidates, Diabetologia, Vol. 48, pp. 1273-1279 (2005), the disclosuresof which are incorporated herein by reference.

Empirically and as scientifically demonstrated supra, when assembledinto distinct pre- and timed post-meal categories, SMBG data follows alog-normal distribution. Consequently, the expected blood glucose valueand predicted error for each meal period are visualized using alog-normal distribution (step 83), as further described infra withreference to FIG. 8. Statistically, each expected blood glucose value isthe geometric mean of the SMBG values stored in the database 14 for theobservational time frame and the predicted error is the standarddeviation of the geometric mean. When the patient's blood glucose andA1C values are within target range, the type of statistical distributionused in the model becomes less crucial. As a result, in a furtherembodiment, a standard normal distribution can be used instead of alog-normal distribution. Under the same rationale, still other types ofstatistical distributions could also be used.

After all of the sets of meal period data have been evaluated andmodeled, an A1c estimate is determined (step 85) for inclusion with thevisualization. In one embodiment, the patient's A1c is derived from meanSMBG values, such as described in C. L. Rohlfing et al., Defining theRelationship Between Plasma Glucose and HbA(1c): Analysis of GlucoseProfiles and HbA(1c) in the Diabetes Control and Complications Trial,Diabetes Care, Vol. 25(2), pp. 275-8 (2002), the disclosure of which isincorporated by reference.

With a circadian profile 61 for a diabetic patient 11, a caregiver isable to apply a “treat to target” approach, as presented through avisual display of glucose management data, that focuses on moving thepatient's SMBG values into target ranges that represent recognizedwell-managed glycemic control, as opposed to merely keeping A1c below acertain point. FIG. 7 is a flow diagram showing a routine 90 forsuperimposing target ranges over the expected blood glucose values andpredicted errors for use in the routine 70 of FIG. 5. The one-to-onecorrespondence between the meal periods in each circadian profile 61 andthe CG&MT mandated target ranges enables the expected blood glucoselevels and the target ranges to be visualized together.

As an initial step in the approach, the target blood glucose levelranges for each meal period are determined for the diabetes patient 11(step 91). The target ranges are then visually superimposed over theexpected blood glucose levels and the ranges (step 92). The targetranges can either be from the CG&MT or as specified by the caregiver. Inone embodiment, different sets of target ranges can be used, including a“default” target range, a gestational diabetes target range (women only)and a target range for use in breaking insulin resistance. The defaulttarget range specifies a pre-meal target of 80 mg/dL<SMBG value<140mg/dL and a post-meal target of 80 mg/dL<SMBG value<180 mg/dL,regardless of whether the meal period is breakfast, lunch or dinner. Thegestational target range decreases the pre-meal target range to 60mg/dL<SMBG value<120 mg/dL. The insulin resistance target range raisesthe pre-meal target to 120 mg/dL<SMBG value<180 mg/dL, which has theaffect of providing the patient 11 with a reason to reduce medicationdosing by moving his SMBG values into the (raised) target range, insteadof continually increasing medication in a futile attempt to reach themandated target range. Once the insulin-resistant diabetes patient 11has achieved the raised insulin resistance breaking target, the defaulttarget range can again be approached in steps.

The treat-to-target approach is equally applicable to controllinghyperglycemic occurrence and hypoglycemic risk, where medication mustrespectively be increased or decreased. The expected blood glucose levelin each meal period is respectively compared to hypoglycemic andhyperglycemic thresholds (steps 93 and 95) and, if a risk exists, themeal period is highlighted and a notice is displayed to inform thepatient 11 and his caregiver (steps 94 and 96). In one embodiment, ahypoglycemic threshold of 50 mg/dL and a post-meal hyperglycemicthreshold of 180 mg/dL are used, where an expected blood glucose levelfalling outside of either threshold will trigger an appropriate warning.The treat-to-target approach also dovetails well with dietaryeducational efforts in which the patient 11 is taught to either decreaseor increase carbohydrate intake to respectively avoid onset ofhyperglycemia or hypoglycemia.

The treat-to-target approach is facilitated through a graphicalvisualization of the model of the expected blood glucose values andpredicted errors with mandated target ranges superimposed. FIG. 8 is auser interface diagram showing, by way of example, an interactive screen100 for visualizing and evaluating the expected blood glucose values andpredicted errors for use in the system 10 of FIG. 1. Since the program15 does not make changes to the patient's course of treatment per se andonly provides guidance, the screen 100 can be used by both the patientand the caregiver, as well as other users.

The visualization groups the expected blood glucose values and theirrespective predicted errors in the model 101 by meal periods 102 for amodel day. The model 101 represents the patient's expected blood glucosevalues 103 and predicted errors 104 before predicted affects ofmedication dosing increments. Each of the meal periods 102 includescategories 105, 106 for a pre-meal and a timed post-meal expected bloodglucose value 103 and a predicted error 104. Due to the log-normaldistribution, the predicted error 104 above and below an expected bloodglucose value 103 is not symmetric and a wider predicted error 104appears above each expected blood glucose value 103 than below. Thetarget ranges 107 are superimposed over each of the expected bloodglucose values 103. When the probability risk is greater than 5%, orother selectable range, that the expected blood glucose values 108, 109will fall below the hypoglycemic threshold, the risk is flagged with awarning 110 displayed to the user. As a result, those meal periods wherepredicted blood glucose values may fall out of target range can bereadily identified by the caregiver and patient alike. Finally, theestimated A1c 111 derived from mean SMBG values is displayed.

The visualization of the expected blood glucose values and predictederrors, and target ranges provides a starting point for the caregiver tobegin working with the patient 11. Changes to diabetes medication,including insulin and oral agents, may be necessary to move the SMBGvalues into target range. FIG. 9 is a flow diagram showing a routine 120for propagating an incremental change in medication dosing for use inthe routine 70 of FIG. 5. The expected glucose values, as modified bythe pharmacodynamics of any medication dosing changes contemplated forthe patient 11, enable the program 15 to suggest quantitative changes tomedication dosing for caregiver or patient 11 consideration. The program15 can also generate qualitative feedback. Generating quantitativefeedback provides a closed-loop treatment model. For safety, allowableincremental changes in medication dosing are limited in amount toachieve the targets more slowly, but safely, and without the risk oflimit cycling. However, to accommodate the slower physiological responseof these smaller incremental changes, the patient 11 has to measure SMBGmore often, up to six times per day, than with a qualitative approachthat works with fewer SMBG values.

The amount of change in medication dosing, based on the diabetesmedications already identified by the patient 11, can be determined byeither incrementally increasing or decreasing the amount of the dosedmedication in the model 101 (step 121). The pharmacodynamics of thediabetes medication is applied in proportion to the incremental changein dosing, as incrementally increased or decreased (step 122), and thevisualization is dynamically adjusted by adding the incremental increaseor decrease in blood glucose level to the expected blood glucose values103 (step 123). The program 15 uses the pharmacodynamics of the diabetesmedications to model the affect on the expected values of near-termblood glucose values and their predicted errors. Drug manufacturersformulate their drugs, so that an incremental change in dosing,amounting to the smallest dosing unit, such as a half tablet of an oralmedication of the lowest strength or one IU of an injectable medication,produces a glucose lowering effect similar to all the otheranti-diabetes drugs in its class. This “normalization” is used to avoidhaving their drug require different dosing profiles when compared tocomparable drugs offered by their competitors, where, for instance, onemanufacturer's medication may require three oral tablets while acompetitor's medication only requires a single oral tablet.

The normalization of comparable anti-diabetes medications is reflectedin the visualization, which allows a user to change medication dosingincrementally (steps 121-123) until the expected blood glucose valuesmove into the target ranges (step 124). The pharmacodynamics allow one“click” on the user interface to reflect a similar glucose loweringaffect for all anti-diabetes drugs in the same class, although thepharmacodynamics of different drug classes are applied in such a way asto normalize the area under the response curves to reflect the totaldrug administered. As a result, longer-acting drugs have a lower peak,but last longer to keep the area under the blood glucose curve similarto the same amount of a shorter-acting drug, which has a higher peak andshort duration of action. That amount of incremental increase ordecrease in the dosed medication is then presented to the caregiver asan incremental suggested change in medication dosing (step 125). Thequalitative scale is slight, moderate, or significant, although in afurther embodiment, this scale can also be expressed quantitatively.Specifically, the program 15 scales six left whole-clicks or twelveright half-clicks to span the range from slight qualifiers (+), tomoderate qualifiers (++), and finally to significant qualifiers (+++)for all dosage increments, or decrements, which allows “clicks” to bequantified into usable measures of dosing, such as half oral tables orIUs of insulin. In addition, the program 15 provides a mechanism forsimply “accepting” or documenting medication changes, such that thosechanges that are pre-populated when the patient 11 enters SMBG readingsand verifies the doses taken.

In a further embodiment, the circadian profile and incremental increaseor decrease in the dosed medication could be uploaded back into thepatient's personal database 14 on his personal or laptop computer 13,mobile computing device, portable media device 12, glucometer 17 orother type of portable blood glucose testing device with onboard datacollection capabilities, as applicable. The circadian profile andincremental change in the dosed medication are uploaded, depending uponwhether the glucometer 17 is used as a dosing treatment controller,where the incremental changes are uploaded, or a regimen guidance tool,in which the incremental changes are maintained separately and offlinefrom the glucometer 17. The decision on how to use the glucometer 17 indiabetes management and uploading the incremental changes into theonboard database turns on whether such usage is thought of as beinginside or outside the treatment arc that connects the output of theglucometer 17 to the “tip of the needle,” that is, the drug deliverydevice. The incremental changes would be uploaded back on to theglucometer 17 if the onboard database is intended to be portable and thepatient 11 intends to run the program 15 on any personal or laptopcomputer 13 or mobile computing device without the benefit of avirtualized database provided through a “cloud” computinginfrastructure. However, incremental change uploading can potentiallybecome cumbersome if the patient 11 has more than one glucometer 17actively in use, such as at home and at work, and keeping the databaseson each of those devices current could become a logistical challenge tothe patient 11 and his caregiver.

In a still further embodiment, a caregiver can model proposed changes tomedication dosing and, through the cloud storage infrastructure, feedthe results back to the patient's personal or laptop computer 13, mobilecomputing device, portable media device 12, glucometer 17 or other typeof portable blood glucose testing device with onboard data collectioncapabilities, as applicable. In addition, manufacturers of glucosemeters and glucose sensing strips that are able to monitor their devicescan also be participants to the caregiving process. These manufacturerscan provide continued calibration and other performance metrics thatwill assure quality, accuracy, and safety in their measuring devices andsignal an unsafe or unreliable status, which would flag a reading aspossibly atypical or unreliable.

Throughout exploration of potential medication dosing changes, includinginsulin and oral agents, the possible affect of any suggested change, orother amount of change in medication dosing desired, is added to thevisualization by dynamically adjusting the expected glucose values basedon the relative pharmacodynamics of the new medication dosing change.FIGS. 10 and 11 are user interface diagrams showing, by way of example,interactive screens 130, 140 for modeling incremental changes inmedication dosing for use in the system 10 of FIG. 1 respectively beforeand after superimposing the target ranges. Referring first to FIG. 10,with the new medication dosing, the two expected blood glucose values131, 132 that formerly risked hypoglycemia at a 5%, or greaterprobability are now safely raised and the risk of hypoglycemia has beenremoved. In addition to the actual medications described (Apidra andLantus), the fields in the descriptor bar for meal periods 102 includeplaceholders at the breakfast, lunch and dinner meal periods forlonger-acting (insulin) medications (“LamB,” “LamL,” “LamD”) and at onlythe bedtime “meal” period for shorter-acting (insulin) medication(“SamS”).

Changes to the dosed medications, whether insulin or oral agents, can beexplored by the user through a control panel 133 (labeled “±Doses toCorrect Rx”). Within the control panel 133, controls 134, 135 under thelabel “Delta” respectively allow the user to explore incrementallyincreasing or decreasing the shorter-acting and longer-actingmedications for a meal period 102, as indicated by a connector line 136.The sub-control button (labeled ‘R’) serves as a shortcut to reset anyexplored increments back to zero. Here, the breakfast meal period 137 isselected with the shorter-acting medication set to Apidra and thelonger-acting medication set to LamB, which is, an as-yet unspecified,longer acting medication at B. To explore the impact of medicationdosing changes during other meal periods 102, for instance, the lunchmeal period 138, the user selects the area labeled “Lunch,” upon whichthe lunch meal period 138 is connected by the connector line 136 to thecontrol panel 133 and the breakfast meal period 137 is deselected.

In one embodiment, the change in insulin dosing can be presented instandard dosage IUs (International Units), in increments of tenths of anIU, where the scaling is rationalized for the patient's delivery device.For instance, hypodermic syringes have a scale that depends on theirfull volume, whereas insulin injection pens dose in increments of 1 IUor 2 IU per click. Insulin pumps are capable of doing in increments of0.1 IU. However, in practice, insulin dosing can be course when the doseis over 10 IU and finer for infants whose dose could be ˜1 IU, such as1.5 IU or 0.5 IU. In suggesting the final change in insulin dosing tothe patient 11 or caregiver, the quantitative dose suggestion couldfollow a conversion of clicks for an insulin injection pen or some otherindividualized scaling factor that depends on the size of the totaldaily dose. Quantifying the clicks to tablets conversion could be by 0.5tablets up to a maximum of 1 to 3 tablets, or limited by the maximummeal and daily allowable amounts.

The expected blood glucose value 103 and predicted error 104 for eachmeal period 102 are adjusted for the pharmacodynamics of the changes tothe diabetes medication being explored. Typically, the pharmacodynamicsfollow the dose-response characteristic. The pharmacodynamics define theeffect of the drugs, that is, the patient's diabetes medication, onblood glucose. The pharmacodynamics of each type of drug is availablefrom the manufacturer. Beginning with the meal period at which thediabetes medication change was administered, the pharmacodynamics areused to raise or lower the expected level of blood glucose in thevisualization until the propagated pharmacodynamics are fully exhausted.Depending upon the particular drug's pharmacodynamics, the expectedblood glucose levels in a sequence of several adjacent meal periods maybe affected. For instance, insulin glargine taken as a basal dose islong-acting and the pharmacodynamics will affect meal periods forseveral days, although the insulin's ability to lower blood glucoselevel after the first 24 hours is significantly diminished. As well,insulin taken as a pre-meal bolus dose is short-acting, yet thepharmacodynamics may well equally propagate for an entire day, albeit ofrelatively small continuing blood glucose level-lowering affect.However, assuming a linear model, the cumulative pharmacodynamics of allof the basal doses and each of the bolus doses taken throughout theobservational time frame may nevertheless lower the expected bloodglucose level at any given meal period more than a single bolus dosewould if taken at that same meal period in isolation from any otherinsulin doses.

Following (or during) the exploration of changes to the medicationdosing, including insulin or oral agents, the target ranges 107 can besuperimposed to provide visual guidance as to whether the new medicationdosing will satisfactorily move the expected blood glucose values 103into the mandated targets and avoid both the risk of hypoglycemia andoccurrence of hyperglycemia. Referring next to FIG. 11, the targetranges 107 have been superimposed above the expected blood glucosevalues 103 and predicted errors 104. All of the patient's expected bloodglucose values 103 are within target and reflect ideal glycemic control.In addition, the caregiver is able to also ensure proper dosing ofmedications through a set of prescription checkers steps (“RxChecks”)141 that includes a control 142 to “Check for possible medicationoverdoses,” which checks that medication is dosed within safe limits ateach meal period and for the entire day. Other types of prescriptionchecks and safeguards are possible.

Atypical SMBG values can also serve to guide the medication dosingadjustment processes. FIG. 12 is a user interface diagram showing, byway of example, an interactive screen 150 for a circadian profile foruse in a further embodiment of the system 10 of FIG. 1. The SMBG values151 and their times of measurement are entered along with an explanation142 that flags an atypical SMBG value for the lunch meal period. Otherlabels within the various interactive screens, such as the labelaccompanying RxChecks steps 141 (shown in FIG. 11), can be highlightedto call attention to unusual events that may lead to atypical SMBG data.Atypical or “unusual” events touch on aspects of the patient's diet,exercise, physical activity, stress, and similar often unavoidableoutcomes of activities of daily living, for example, eating an atypicalamount of carbohydrates (either more or less than normal, as happens onThanksgiving Day) without a matching correction bolus, experiencing moreor less stress than usual, or engaging in an unusual amount of exerciseor physical activity.

In one embodiment, a flagged event triggers the display of a notice to atooltip associated with the associated post- and mid-meal glucoseranges, that is, “After L” and “Mid L-D.” Here, the notice would say,for instance, “Unusual ±CHO or unusual activity in this MP may distortthis prediction.” Similarly, for the following pre-meal glucose range inthe following meal period, that is, “Pre D,” the notice would say, forinstance, “Preceding unusual ±CHO or unusual activity may distort thisprediction.” Also, as a further guide in deciding whether to accept orignore a potentially atypical expected blood glucose value and itsrange, a tooltip can also be associated with the low end of thepredicted range 108 (shown in FIG. 8) for a meal period category, suchas “Before L,” that includes the acceptable SMBG reading in the rangefor that meal category. This tooltip notice can be helpful inunderstanding why a warning about a factitious risk of hypoglycemia thatarises from an outlying hyperglycemia event can safely be ignored. Othertypes and triggers of notice are possible.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A computer-implemented method for facilitatingaccurate glycemic control by modeling blood glucose using cloud-basedcircadian profiles, comprising the steps of: defining a plurality ofmeal periods that each occurs each day at a set time; defining aplurality of classes that each class comprises a set ofanti-hyperglycemic medications that has similar glucose loweringeffects; building a circadian profile for a diabetic patient, comprisingthe steps of: choosing an observational time frame for the circadianprofile comprising a plurality of days that have occurred recently; andstoring online at least two sets of pre- and post-meal period data thatcomprise blood glucose levels, doses of anti-hyperglycemic medicationsthat were respectively taken during each of the meal periods for whichthe blood glucose levels were recorded in a cloud computinginfrastructure, and the classes to which the anti-hyperglycemicmedications are comprised; and creating a model of glucose managementthrough the circadian profile for the diabetic patient for each of theanti-hyperglycemic medications in the class over the cloud computinginfrastructure, comprising: validating access to the circadian profile;defining a modeling period comprising a plurality of days, which eachcomprises the same plurality of the meal periods that occurred each dayin the observational time frame; estimating expected blood glucosevalues and their predicted errors at each of the meal periods from theblood glucose levels in each data in the validated circadian profilethat respectively occur at the same set times; visualizing the expectedblood glucose values and their predicted errors over time for each mealperiod in a log-normal distribution; and selecting one of the mealperiods and, for each anti-hyperglycemic medication in the class,modeling a change in the dose of the anti-hyperglycemic medication forthe selected meal period, comprising the steps of: obtaining glucoselowering effect of the modeled change in the dose of theanti-hyperglycemic medication and glucose lowering effects of otheranti-hyperglycemic medications in the class; normalizing the glucoselowering effect of the modeled change in the dose of theanti-hyperglycemic medication with the glucose lowering effects of theother anti-hyperglycemic medications in the class; propagating thenormalized blood glucose lowering effect over time for the modeledchange in the dose of the anti-hyperglycemic medication to the expectedblood glucose values, beginning with the selected meal period andcontinuing with each of the meal periods occurring subsequently in themodeling period, the normalized blood glucose lowering effect beingadjusted in proportion to the set time of each subsequent meal perioduntil the normalized blood glucose lowering effect is exhausted; andvisualizing the expected blood glucose values as propagated and theirpredicted errors in the log-normal distribution, wherein the steps areperformed on a suitably-programmed computer.
 2. A method according toclaim 1, further comprising the step of: determining target ranges forblood glucose at each of the meal periods occurring each day in themodeling period and superimposing the target ranges over the expectedblood glucose values for each meal period in the log-normaldistribution.
 3. A method according to claim 1, further comprising thesteps of: associating a flag with at least one of the blood glucoselevels as an atypical measurement; and processing the flagged bloodglucose level in the circadian profile comprising at least one of:treating the flagged blood glucose level differently from other bloodglucose levels; and discarding the flagged blood glucose level from thecircadian profile.
 4. A method according to claim 3, further comprisingthe steps of: defining the atypical measurement based on at least one ofa level of carbohydrate intake, physical activity, and stress of thediabetic patient; determining a weighting criteria for the flagged bloodglucose level; and assigning a weight to the flagged blood glucoselevel.
 5. A method according to claim 1, further comprising the step of:determining the classes of the anti-hyperglycemic medications based onthe glucose lowering effect comprising at least one of short-acting,intermediate-acting, and long-acting.
 6. A method according to claim 5,further comprising the step of: identifying the glucose lowering effectby at least one of hours, days, weeks, months, and years.
 7. A methodaccording to claim 1, further comprising the step of: modeling a kind ofanti-hyperglycemic medication comprising at least one of oralanti-hyperglycemic medication and injectable anti-hyperglycemicmedication.
 8. A method according to claim 1, further comprising thestep of: defining the meal periods as comprising, within each day in theobservational time frame, breakfast, lunch, dinner, and bedtime mealperiods.
 9. A method according to claim 1, further comprising the stepof: integrating further information into the meal period data in thecircadian profile comprising at least one of body weight of the diabeticpatient, times of measurement of the blood glucose levels, site ofinjection of the anti-hyperglycemic medication, and comments onlifestyle.
 10. A method according to claim 1, further comprising thestep of: providing the modeled change in the dose of theanti-hyperglycemic medication as dose suggestion comprising at least oneof relative dose suggestion and absolute dose suggestion.
 11. A methodaccording to claim 1, further comprising at least one of the steps of:presenting a qualitative scale for the dose suggestion comprising scalesof slight change, moderate change, and significant change; andpresenting a quantitative scale of the dose suggestion comprising aquantum of the anti-hyperglycemic medication.
 12. A method according toclaim 1, further comprising the steps of: including a body weight of thediabetic patient in the circadian profile; and performing a trendanalysis of the body weight over any preceding observational time framesfor the selection of the change in the dose of the anti-hyperglycemicmedication.
 13. A computer-implemented system for facilitating accurateglycemic control by modeling blood glucose using cloud-based circadianprofiles, comprising: an electronically-stored database maintained in acloud computing infrastructure and comprising a plurality of records,each record comprising a circadian profile, comprising: a plurality ofmeal periods that each occurs each day at a set time and divide eachcircadian profile into the meal periods; a plurality of classes thateach class comprises a set of anti-hyperglycemic medications that hassimilar glucose lowering effects; an observational time frame comprisinga plurality of days that have occurred recently; at least two of typicalmeasurements of pre-meal and post-meal self-measured blood glucose thatwere recorded at each of the meal periods that occurred each day in theobservational time frame; a dose of anti-hyperglycemic medication thatwas taken during each of the meal periods for which the blood glucosemeasurements were recorded; and one of the classes to which the dose ofthe anti-hyperglycemic medication is comprised; a user interface forcreating a model of glucose management through the circadian profile forthe diabetic patient for each of the anti-hyperglycemic medications inthe class, comprising: an executable application configured to modelglucose management, comprising: a validation module configured tovalidate access to the circadian profiles through the cloud computingenvironment; a model period module configured to define a modelingperiod comprising a plurality of days, which each comprises the sameplurality of the meal periods that occurred each day in theobservational time frame; a collection module configured to collect theblood glucose measurements, upon validation, comprising each of the mealperiods; a statistical engine configured to determine expected bloodglucose values and their predicted errors at each of the meal periodsfrom the blood glucose measurements based on the meal periods in thecircadian profile; a log-normal distribution module configured tovisualize the expected blood glucose values and their predicted errorsover time for each meal period in a log-normal distribution; and achange modeling module configured to select one of the meal periods and,for each anti-hyperglycemic medication in the class, to model a changein the dose of the anti-hyperglycemic medication for the selected mealperiod, comprising: an effect module configured to obtain glucoselowering effect of the modeled change in the dose of theanti-hyperglycemic medication and glucose lowering effects of otheranti-hyperglycemic medications in the class; a normalization moduleconfigured to normalize the glucose lowering effect of the modeledchange in the dose of the anti-hyperglycemic medication with the glucoselowering effects of the other anti-hyperglycemic medications in theclass; a propagation module configured to propagate the normalizedglucose lowering effect over time for the modeled change in the dose ofthe anti-hyperglycemic medication to the expected blood glucose values,beginning with the selected meal period and continuing with each of themeal periods occurring subsequently in the modeling period, thenormalized blood glucose lowering effect being adjusted in proportion tothe set time of each subsequent meal period until the normalized bloodglucose lowering effect is exhausted; and a visualization moduleconfigured to visualize the expected blood glucose values as propagatedand their predicted errors in the log-normal distribution; and a controlpanel for operating the change in the dose of the anti-hyperglycemicmedication; and a display for displaying the model of glucose managementthrough the circadian profile.
 14. A system according to claim 13,further comprising: a flag module configured to associate a flag with atleast one of the recorded blood glucose measurements as an atypicalmeasurement; and a flag process module configured to process the flaggedblood glucose measurement in the circadian profile comprising at leastone of: a flag weight module configured to treat the flagged bloodglucose measurement differently from other recorded blood glucosemeasurements; and a deletion module configured to discard the flaggedblood glucose measurement from the circadian profile.
 15. A systemaccording to claim 14, further comprising: a classification moduleconfigured to define the atypical measurement based on at least one of alevel of carbohydrate intake, physical activity, and stress of thediabetic patient; a criteria module configured to determine a weightingcriteria for the flagged blood glucose measurement; and a weightassignment module configured to assign a weight to the flagged bloodglucose measurement.
 16. A system according to claim 15, furthercomprising: a threshold of at least one of hypoglycemic risk andhyperglycemic occurrence, which are both expressed as blood glucosevalues stored in the database; a warning module configured to identifyeach of the expected blood glucose values exhibiting either a risk offalling below the hypoglycemic risk threshold or rising above thehyperglycemic occurrence threshold; and a tooltip for indicating anotice associated with the atypical measurement on the display andexplaining a reason why the risk can be ignored.
 17. A system accordingto claim 13, further comprising: target ranges stored in the databasefor the expected blood glucose values at each meal period in the modelday; a target module configured to superimpose the target ranges overthe visualized expected blood glucose values; a change control on thecontrol panel for at least one of increasing and decreasing the dose ofthe anti-hyperglycemic medication for the meal period on the display,the increasing and decreasing of the anti-hyperglycemic medication beingoperated by clicking the change control on the control panel; anadjustment module configured to adjust the expected blood glucosevalues, comprising at least one of: a dose increase module configured toincrease the dose of the anti-hyperglycemic medication until theexpected blood glucose value for the meal period moves into the targetrange based on the normalized glucose lowering effect of the change inthe dose of the anti-hyperglycemic medication; and a dose decreasemodule configured to decrease the dose of the anti-hyperglycemicmedication until the expected blood glucose value for the meal periodmoves into the target range based on the normalized glucose loweringeffect of the change in the dose of the anti-hyperglycemic medication;and a dose suggestion module configured to determine the changes in thedose of the anti-hyperglycemic medication as a dose suggestion.
 18. Asystem according to claim 17, further comprising: a quantum moduleconfigured to define a quantum and unit of the anti-hyperglycemicmedication from the normalized glucose lowering effect of theanti-hyperglycemic medication applied to the expected blood glucosevalue as the dose suggestion, comprising: a count module configured tocount a number of the clicks of the change control on the control panelfor the adjustment of the expected blood glucose value into the targetrange; and a conversion module configured to covert the number of theclicks into a quantum of the anti-hyperglycemic medication.
 19. A systemaccording to claim 13, further comprising: a medication class moduleconfigured to determine the classes of the anti-hyperglycemicmedications based on the glucose lowering effect comprising at least oneof short-acting, intermediate-acting, and long-acting.
 20. A systemaccording to claim 19, further comprising: a temporality moduleconfigured to identify the glucose lowering effect by at least one ofhours, days, weeks, months, and years.
 21. A system according to claim13, further comprising: a medication kind module configured to model akind of anti-hyperglycemic medication comprising at least one of oralanti-hyperglycemic medication and injectable anti-hyperglycemicmedication.
 22. A system according to claim 13, further comprising: ameal period module configured to define the meal periods as comprising,within each day in the observational time frame, breakfast, lunch,dinner, and bedtime meal periods.
 23. A system according to claim 13,further comprising: an integration module configured to integratefurther information into the meal period data in the circadian profilecomprising at least one of body weight of the diabetic patient, times ofmeasurement of the blood glucose measurements, site of injection of theanti-hyperglycemic medication, and comments on lifestyle.
 24. A systemaccording to claim 13, further comprising: a suggestion moduleconfigured to provide the modeled change in the dose of theanti-hyperglycemic medication as dose suggestion comprising at least oneof relative dose suggestion and absolute dose suggestion.
 25. A systemaccording to claim 24, further comprising at least one of: a qualitativescale for the dose suggestion comprising scales of slight change,moderate change, and significant change; and a quantitative scale forthe dose suggestion comprising a quantum of the anti-hyperglycemicmedication.